#n= 200 k= 6 p= 0.5
#sigma: [1, 2, 3, 4, 5, 6] A [1, 3, 8, 10]
#sample size array: [100, 1000, 10000]
***** starting execution of DypChip with all profiles ******
[4.59787323e-27 4.59787323e-27 4.59787323e-27 7.32075358e-01
 0.00000000e+00 2.67924642e-01 0.00000000e+00 0.00000000e+00
 0.00000000e+00]
for m= 100 err_vec= [np.float64(0.017924641841648548), np.float64(0.017924641841648437), np.float64(4.5978732330868696e-27), np.float64(4.5978732330868696e-27), np.float64(4.5978732330868696e-27)]
***** starting execution of DypChip with all profiles ******
[4.59787323e-27 4.59787323e-27 4.59787323e-27 7.32075358e-01
 0.00000000e+00 2.67924642e-01 0.00000000e+00 0.00000000e+00
 0.00000000e+00]
for m= 1000 err_vec= [np.float64(0.0029246418416485342), np.float64(0.0029246418416484232), np.float64(4.5978732330868696e-27), np.float64(4.5978732330868696e-27), np.float64(4.5978732330868696e-27)]
***** starting execution of DypChip with all profiles ******
[4.59787323e-27 4.59787323e-27 4.59787323e-27 7.32075358e-01
 0.00000000e+00 2.67924642e-01 0.00000000e+00 0.00000000e+00
 0.00000000e+00]
for m= 10000 err_vec= [np.float64(0.0014246418416485884), np.float64(0.001424641841648422), np.float64(4.5978732330868696e-27), np.float64(4.5978732330868696e-27), np.float64(4.5978732330868696e-27)]
